Instructions to use danish-foundation-models/DFM-Mimir-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danish-foundation-models/DFM-Mimir-v1.5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="danish-foundation-models/DFM-Mimir-v1.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("danish-foundation-models/DFM-Mimir-v1.5")
model = AutoModelForCausalLM.from_pretrained("danish-foundation-models/DFM-Mimir-v1.5", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use danish-foundation-models/DFM-Mimir-v1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danish-foundation-models/DFM-Mimir-v1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "danish-foundation-models/DFM-Mimir-v1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/danish-foundation-models/DFM-Mimir-v1.5

SGLang

How to use danish-foundation-models/DFM-Mimir-v1.5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "danish-foundation-models/DFM-Mimir-v1.5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "danish-foundation-models/DFM-Mimir-v1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "danish-foundation-models/DFM-Mimir-v1.5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "danish-foundation-models/DFM-Mimir-v1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use danish-foundation-models/DFM-Mimir-v1.5 with Docker Model Runner:
```
docker model run hf.co/danish-foundation-models/DFM-Mimir-v1.5
```

DFM-Mimir-v1.5 / comparison_macro_averages.csv

peter-sk

Add completed Ouro 1.4B and 2.6B benchmark comparisons

521b40b verified 10 days ago

Raw History Blame Contribute Delete

1.29 kB

	model,English,Math/Code,Danish
	Mimir-2850K,77.80660975659963,71.11563182258054,62.97261975652402
	Mimir-1650K,68.9650039377685,64.14130685355374,57.035111127408825
	Qwen3.5-0.8B,50.64696385659728,38.59197741575596,30.887131406593525
	Qwen3.5-2B,58.66609059044379,58.97105555452825,32.73121462552333
	Qwen3.5-4B,69.26109759303107,65.02305318762058,49.823394609268526
	Qwen3.5-9B,77.05400488468159,85.83726412593921,53.053378634996164
	Gemma-4-E2B,52.78615319483545,75.4416586845171,44.432706066784405
	Gemma-4-E4B,54.89259286806845,82.85323545183898,53.58009604944023
	Gemma-4-E4B-thinking,72.25952457735038,82.17406448590641,54.52621177814698
	Gemma-3-1B,37.44421641519764,43.187253092697716,34.00180249659065
	OLMo-2-1B,42.81713873193756,31.35227081368615,25.722888329168775
	SmolLM3-3B,63.07699902723909,67.93006761712803,28.816229975105003
	EuroLLM-9B,60.61038405367872,17.297073170731707,45.73833917773132
	Apertus-8B,61.631183451723984,44.10703748220197,46.520369456632864
	Ministral-3-8B,71.34070074726007,76.23670161553775,42.23503478769814
	Munin-Apertus-8B,,,44.35326568631533
	Munin-Mistral-8B,,,42.13241394734135
	Munin-Qwen-9B,,,44.79791433886715
	Ouro-1.4B,60.678463981148056,56.63173776635417,24.798753642496514
	Ouro-2.6B,70.2257023084669,60.8018983339189,25.392804181009986